crowact 0.1.3

A lightweight ReAct agent framework with tool calling, streaming chunks, and pluggable providers.

CrowAct

crowact is a lightweight ReAct agent framework with tool calling, streaming chunks, and pluggable providers.

Distribution name: crowact
Python import name: CrowAct

Requirements

• Python 3.10+
• requests
• python-dotenv

Install

pip install crowact

For local development in this repository:

pip install -e .

Quick Start

from CrowAct import Agent, LLMProvider, load_prompt_from
from CrowAct.agent.tools import get_tools

TOOLS = get_tools("test_folder")


def main() -> None:
    provider = LLMProvider.from_anthropic_env("byte2.env")
    agent = Agent(
        provider=provider,
        model="deepseek-v3.2",
        system_prompt=load_prompt_from(["system.md", "rules.txt"]),
        tools=TOOLS,
    )

    question = (
        "Please use tools to calculate this step by step: "
        "first compute 1+19, then take the square root of the result, "
        "and finally add 100."
    )

    for chunk in agent.run(question, stream=True):
        print(chunk)

    print(agent.last_answer)


if __name__ == "__main__":
    main()

load_prompt_from(...) supports either a single file path or multiple file paths.

system_prompt = load_prompt_from("system.md")

system_prompt = load_prompt_from(["system.md", "rules.txt"])

Generated format:

system.md
----
file content

rules.txt
----
file content

Tool Files

get_tools(folder) loads every *.py file in the target folder and registers functions marked with the @tool(...) decorator.

Example folder:

test_folder/
  tool1.py
  tool2.py

Example tool:

from CrowAct.agent.tools import tool


@tool(
    description="Compute the square root of a number.",
    param_descriptions={"number": "The number to take the square root of"},
)
def sqrt_tool(number: float) -> float:
    return number ** 0.5

Agent Output

Agent.run(...) always returns an iterator of chunks.

Chunk types:

• {"type": "text", "text": "..."}
• {"type": "tool_use", "id": "...", "name": "...", "input": {...}}
• {"type": "tool_result", "tool_use_id": "...", "content": "..."}

Behavior:

• stream=True: text is yielded incrementally as streaming chunks.
• stream=False: text is yielded as complete blocks, but the API remains iterator-based.

The final plain-text answer is stored in agent.last_answer.

History

history is optional. If omitted, the Agent starts with an empty history and keeps appending records across later run() calls. Stored history includes user messages, assistant messages, tool calls, and tool results. The history is available at agent.history and is truncated from the front with history_window (default: 20).

agent = Agent(
    provider=provider,
    model="deepseek-v3.2",
    system_prompt="You are helpful.",
    history_window=20,
)
agent.run("First question")
agent.run("Follow-up question")
print(len(agent.history))  # <= 20

Provider Setup

Anthropic-style

provider = LLMProvider.from_anthropic_env(".env")

Expected environment variables:

ANTHROPIC_BASE_URL=https://your-endpoint
ANTHROPIC_API_KEY=your-api-key

Endpoint pattern:

• .../v1/messages

OpenAI-style

provider = LLMProvider.from_openai_env(".env")

Expected environment variables:

OPENAI_BASE_URL=https://your-endpoint
OPENAI_API_KEY=your-api-key

Endpoint pattern:

• .../chat/completions

Current Limitation

LLMProvider supports both anthropic and openai endpoint styles, but the request body and tool-calling flow are currently implemented around the Anthropic-style message format. If you use an OpenAI-compatible endpoint, confirm that it accepts the same request shape before relying on it.

Local Example

This repository includes:

• main.py
• test_folder/tool1.py
• test_folder/tool2.py

License

Add a license before publishing to PyPI.